The Society for Computer Applications in Radiology (SCAR) is a 501(c)(3) philanthropic organization committed to the advancement of computer applications and information technology in medical imaging through education and research. SCAR intends to sponsor a scientific meeting to spearhead research, education and discovery of innovative solutions to the problem of healthcare information and image data overload. It is anticipated that the radiological community will have to shift its image interpretation and management processes to accommodate the very large medical image data sets that will be acquired by current and future digital imaging devices. In June of 2003 SCAR announced the TRIP TM (Transforming the Radiological Interpretation Process) Initiative to foster interdisciplinary research on technological as well as environmental and human factors to better manage and exploit the massive amount of information available. The solution will ultimately involve advances in several major concept areas of medical imaging informatics including human perception, image processing and computer aided detection, image and data visualization, navigation and user-interface design, databases and integration, and evaluation and validation of methods and performance. SCAR aims to stimulate interdisciplinary thinking about this problem and discussion of solutions through a series of focused research meetings beginning with a two-day conference to be held October 4-5, 2004 in Washington, D.C. Plenary Sessions of invited speakers will include experts from areas outside of medicine discussing their approaches to dealing with large image data sets, followed by invited speakers in the key topic areas listed above. These will be followed by Break-Out Discussion Sessions and supplemented by a Poster Session of abstracts submitted by attendees. Intended participants include radiologists and other physicians, biomedical engineers, biomedical imaging scientists, computer scientists, imaging informatics scientists, imaging physicists, information technology professionals, medical imaging industry personnel, and any other students or professionals interested in the problem of very large medical image data sets.